In manufacturing semiconductors, the silicone wafers undergo in-process checks, including foreign substance inspection, visual inspection, and SEM inspection, in each process such as exposure and etching. The inspection images obtained through these inspections are classified and analyzed by size, shape and location of the foreign substance causing a defect and the result is utilized to improve the yield.
A method of classifying these images automatically by characteristics is disclosed in Japanese application patent laid-open publication No. Hei 08-21803. With this method, a classification reference image called “teaching image data” is provided and the images are classified according to this reference image automatically. To be concrete, the Publication covers a defect type judgment device for judging the type of individual defect found through a defect inspection of a specimen; wherein said device is equipped with a neuro-processing unit which converts an input pattern into an optional output pattern; said neuro-processing unit has been so taught that an output pattern representing an optional defect type is displayed for each defect information of the input pattern corresponding to each defect type; the defect information of the defect found through the defect inspection is input into the neuro-processing unit; and the defect type is determined in accordance with the output pattern from the unit.
Electronic devices, typically semiconductors are made through multiple processes, including exposure, development and etching on a wafer, to form a substrate. For a wafer worked in one of those multiple processes, information about the location, size, quantity, type, etc. of the foreign substance (hereinafter called the defect) collected on the wafer and information about the processing size of the workpiece are obtained, as needed, by a foreign substance inspection device, visual inspection device and/or SEM inspection device. It is reported on pages 88, 99 and 102 of 1996.8 issue of the monthly Semiconductor World that all inspection data are generally sent to an analyzing system via a network and managed and analyzed by the system.
In some processes, defect images are obtained using an electronic microscope and the actual shape and size of the defect are classified so as to identify the cause of the defect. This classification is done visually on the display of a personal computer, where the obtained defect images are classified into groups of similar defect by size, shape, and so on. The defect images are obtained at several points on each wafer, which number of points is determined manually making reference to the distribution or wafer map of the defect in the pre-process, or hundreds of defect images are obtained in an hour by the ADR (Automatic Defect Review) function. In either case, the number of images to be obtained tends to increase.
In the selection procedure of the “teaching image data” (hereinafter called the instruction), user strongly wants to set the data for himself in accordance with his own manufacturing line and/or criteria. In other words, it is required that the classification by sub class, which is unique to each user, is made possible in addition to the automatic classification based on the instruction by an inspection device as shown in the afore-mentioned disclosed patent.
As the inspection data volume has increased, the selection procedure of the “teaching image data” has become important to maintain accurate analysis of the inspection images. Besides, the review itself of a vast amount of the inspection image data has become tough to the user. Both maintenance of the analytical accuracy of the inspection images and increase of the review speed have become essential to improve the production efficiency.